Scientific Computing and Linux CHUGALUG Meeting Lachele Foley

My Background ● Linux & scientific computing start together – Dissertation with major computational component

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My Background ● Computational emphasis when teaching – Students had to use Linux, compile programs, etc.

My Background ● Currently researcher/sysadmin for comp. lab. – Complex Carbohydrate Research Center (UGA) – ● or search on ”Glycam” to learn more

Scientific Computing ● Two categories: – Users of pre-existing scientific software – Persons involved in generation of the software ● Programmers ● Theoreticians ● Engineers ● (etc.) ● Our lab does both

Pre-existing Software ● Usually written by scientists, not programmers ● Dictated by practicality, probably won't change ● Often not (or minimally) user-friendly ● Mostly ok because scientists are used to that... :-) ● Can be out of date, not-quite-portable, etc. ● Just installing it is sometimes a nightmare ● Not all is bad! ● Some is also very good, portable, etc.

Pre-existing Software Issues ● Not so different from other software: ● Hastily written by whoever has the time ● Might have very specific pre-requisites ● upgrading the OS breaks package one ●...but package two requires the bleeding-edge newest ● Portability problems ● installing from sources often fixes this

A Bad Day ● Need to make simple-ish modification to a file ● There is a script already written – Just need to install it – Yay!

A Bad Day ● It requires the absolute newest numpy ●...which requires the newest python ●...which requires the newest gcc ●...which requires mpid ●...and gmp

A Bad Day ● The script requires the absolute newest numpy ●...which requires the newest python ●...which requires the newest gcc ●...which requires mpid ●...and gmp ●...and about 8 hours

Getting Better, Mostly ● Scientists adopt standard practices ● e.g.,./configure; make; make install ● Internet eases learning curves ● ”Portability” is a commonly known term ●...as is ”backwards compatibility”, etc. ● Larger projects hire programmers, write GUI's

When There Is No Software ● Research – New methods – Not yet implemented old methods – Hybrid methods – Parameter and instruction-set development ● Linux's flexibility makes it appealing ●...but can also be a source of stress

”...but program X will do that.” 1. Obtain initial data from somewhere 2. Use program Z to convert to new format 3. Write script to rearrange format from 2 for simulation program Y 4. Write another script for running program Y in unusual manner 5. Run simulation program Y 6. Extract results from Y with post-processing program X 7. Write script to rearrange data from 6 for math package W 8. Run math package W 9. Write script to merge results from 8 with original data

Some Utilities Exist ● Flexible software packages ● VMD, Chimera ● Scripting languages ● MMTK, NAB ● Features – Ready-made, easy & modular for common tasks – Protection from having to write code (but can...)

Easier Just To Code It ● The bottleneck is data read/reformat ● Includes some complex stuff like pattern recognition ● The ”hard” parts – e.g., the math – often easiest ● ”So, use Perl or Python, etc.” (I know Perl) ● arrays of hashes of arrays indexed to other arrays ●...and loose memory management means mistakes ● If complex, I find it's easier just to go back to C

So, a Library... ● Not a radical idea – MMTK is fairly similar ● Use it rather than – finding another program to install – learning a new scripting language – learning yet another programming language – stealing other folks' code ● (open is not necessarily free...)

GLYLIB ● GLYLIB because ”glyco” for ”sugar” ● Centralized data structures – intuitive and easily extensible ● Modular functions – most operate directly on central data ● In plain C – ANSI/POSIX simplicity wherever possible

GLYLIB ● – also: glycam.ccrc.uga.edu/glylib